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  <h1>Source code for torch_tensorrt.fx.lower</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">dataclasses</span> <span class="k">as</span> <span class="nn">dc</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span>

<span class="c1"># @manual=//deeplearning/trt/python:py_tensorrt</span>
<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.fx</span> <span class="k">as</span> <span class="nn">fx</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch_tensorrt.fx.tracer.dispatch_tracer.aten_tracer</span> <span class="k">as</span> <span class="nn">aten_tracer</span>
<span class="kn">from</span> <span class="nn">torch.fx.passes.splitter_base</span> <span class="kn">import</span> <span class="n">SplitResult</span>

<span class="kn">from</span> <span class="nn">.fx2trt</span> <span class="kn">import</span> <span class="n">TRTInterpreter</span><span class="p">,</span> <span class="n">TRTInterpreterResult</span>
<span class="kn">from</span> <span class="nn">.lower_setting</span> <span class="kn">import</span> <span class="n">LowerSetting</span>
<span class="kn">from</span> <span class="nn">.passes.lower_pass_manager_builder</span> <span class="kn">import</span> <span class="n">LowerPassManagerBuilder</span>
<span class="kn">from</span> <span class="nn">.passes.pass_utils</span> <span class="kn">import</span> <span class="n">PassFunc</span><span class="p">,</span> <span class="n">validate_inference</span>
<span class="kn">from</span> <span class="nn">.tools.timing_cache_utils</span> <span class="kn">import</span> <span class="n">TimingCacheManager</span>
<span class="kn">from</span> <span class="nn">.tools.trt_splitter</span> <span class="kn">import</span> <span class="n">TRTSplitter</span><span class="p">,</span> <span class="n">TRTSplitterSetting</span>

<span class="kn">from</span> <span class="nn">.tracer.acc_tracer</span> <span class="kn">import</span> <span class="n">acc_tracer</span>
<span class="kn">from</span> <span class="nn">.trt_module</span> <span class="kn">import</span> <span class="n">TRTModule</span>
<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">LowerPrecision</span>

<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>

<span class="n">Input</span> <span class="o">=</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span>


<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
    <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
    <span class="nb">input</span><span class="p">,</span>
    <span class="n">min_acc_module_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
    <span class="n">max_batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2048</span><span class="p">,</span>
    <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">25</span><span class="p">,</span>
    <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
    <span class="n">lower_precision</span><span class="o">=</span><span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">,</span>
    <span class="n">verbose_log</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
    <span class="n">timing_cache_prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
    <span class="n">save_timing_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
    <span class="n">cuda_graph_batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
    <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
    <span class="n">is_aten</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
    <span class="n">use_experimental_fx_rt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Takes in original module, input and lowering setting, run lowering workflow to turn module</span>
<span class="sd">    into lowered module, or so called TRTModule.</span>

<span class="sd">    Args:</span>
<span class="sd">        module: Original module for lowering.</span>
<span class="sd">        input: Input for module.</span>
<span class="sd">        max_batch_size: Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
<span class="sd">        min_acc_module_size: Minimal number of nodes for an accelerated submodule</span>
<span class="sd">        max_workspace_size: Maximum size of workspace given to TensorRT.</span>
<span class="sd">        explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</span>
<span class="sd">        lower_precision: lower_precision config given to TRTModule.</span>
<span class="sd">        verbose_log: Enable verbose log for TensorRT if set True.</span>
<span class="sd">        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.</span>
<span class="sd">        save_timing_cache: Update timing cache with current timing cache data if set to True.</span>
<span class="sd">        cuda_graph_batch_size: Cuda graph batch size, default to be -1.</span>
<span class="sd">        dynamic_batch: batch dimension (dim=0) is dynamic.</span>
<span class="sd">        use_experimental_fx_rt: Uses the next generation TRTModule which supports both Python and TorchScript based execution (including in C++).</span>
<span class="sd">    Returns:</span>
<span class="sd">        A torch.nn.Module lowered by TensorRT.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">use_experimental_fx_rt</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">explicit_batch_dimension</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="s2">&quot;The experimental unifed runtime only supports explicit batch. Please make sure to set explicit_batch_dimension=True when use_experimental_fx_rt=True&quot;</span>
        <span class="p">)</span>

    <span class="n">lower_setting</span> <span class="o">=</span> <span class="n">LowerSetting</span><span class="p">(</span>
        <span class="n">max_batch_size</span><span class="o">=</span><span class="n">max_batch_size</span><span class="p">,</span>
        <span class="n">min_acc_module_size</span><span class="o">=</span><span class="n">min_acc_module_size</span><span class="p">,</span>
        <span class="n">max_workspace_size</span><span class="o">=</span><span class="n">max_workspace_size</span><span class="p">,</span>
        <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="n">explicit_batch_dimension</span><span class="p">,</span>
        <span class="n">lower_precision</span><span class="o">=</span><span class="n">lower_precision</span><span class="p">,</span>
        <span class="n">verbose_log</span><span class="o">=</span><span class="n">verbose_log</span><span class="p">,</span>
        <span class="n">timing_cache_prefix</span><span class="o">=</span><span class="n">timing_cache_prefix</span><span class="p">,</span>
        <span class="n">save_timing_cache</span><span class="o">=</span><span class="n">save_timing_cache</span><span class="p">,</span>
        <span class="n">cuda_graph_batch_size</span><span class="o">=</span><span class="n">cuda_graph_batch_size</span><span class="p">,</span>
        <span class="n">dynamic_batch</span><span class="o">=</span><span class="n">dynamic_batch</span><span class="p">,</span>
        <span class="n">is_aten</span><span class="o">=</span><span class="n">is_aten</span><span class="p">,</span>
        <span class="n">use_experimental_rt</span><span class="o">=</span><span class="n">use_experimental_fx_rt</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="n">lowerer</span> <span class="o">=</span> <span class="n">Lowerer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">lower_setting</span><span class="o">=</span><span class="n">lower_setting</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">lowerer</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="nb">input</span><span class="p">)</span></div>


<span class="nd">@dc</span><span class="o">.</span><span class="n">dataclass</span>
<span class="k">class</span> <span class="nc">LowerTrtInterpreter</span><span class="p">:</span>
    <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span>
    <span class="n">timing_cache_manager</span><span class="p">:</span> <span class="n">TimingCacheManager</span>

    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">create</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">):</span>
        <span class="n">timing_cache_manager</span> <span class="o">=</span> <span class="n">TimingCacheManager</span><span class="p">(</span>
            <span class="n">lower_setting</span><span class="o">.</span><span class="n">timing_cache_prefix</span><span class="p">,</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">save_timing_cache</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">LowerTrtInterpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">,</span> <span class="n">timing_cache_manager</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">split_name</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TRTInterpreterResult</span><span class="p">:</span>
        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="p">,</span> <span class="s2">&quot;Can&#39;t find input specs for lowering!&quot;</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;split_name=</span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">, input_specs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="p">)</span>

        <span class="c1"># Prepare algorithm selector and timing_cache for TRTInterpreter</span>
        <span class="n">algo_selector</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">algo_selector</span><span class="p">:</span>
            <span class="n">algo_selector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">algo_selector</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">.json&quot;</span><span class="p">)</span>
        <span class="n">cache_data</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="p">:</span>
            <span class="k">try</span><span class="p">:</span>
                <span class="n">cache_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="o">.</span><span class="n">get_timing_cache_trt</span><span class="p">(</span><span class="n">split_name</span><span class="p">)</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Timing cache is used!&quot;</span><span class="p">)</span>
            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Cannot load timing cache for </span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
                <span class="n">cache_data</span> <span class="o">=</span> <span class="kc">None</span>

        <span class="n">interpreter</span> <span class="o">=</span> <span class="n">TRTInterpreter</span><span class="p">(</span>
            <span class="n">mod</span><span class="p">,</span>
            <span class="n">input_specs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="p">,</span>
            <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span><span class="p">,</span>
            <span class="n">explicit_precision</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_precision</span><span class="p">,</span>
            <span class="n">logger_level</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">VERBOSE</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">verbose_log</span>
            <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">WARNING</span><span class="p">,</span>
        <span class="p">)</span>

        <span class="n">interp_result</span><span class="p">:</span> <span class="n">TRTInterpreterResult</span> <span class="o">=</span> <span class="n">interpreter</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
            <span class="n">max_batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
            <span class="n">max_workspace_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">max_workspace_size</span><span class="p">,</span>
            <span class="n">lower_precision</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">lower_precision</span><span class="p">,</span>
            <span class="n">strict_type_constraints</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">strict_type_constraints</span><span class="p">,</span>
            <span class="n">algorithm_selector</span><span class="o">=</span><span class="n">algo_selector</span><span class="p">,</span>
            <span class="n">timing_cache</span><span class="o">=</span><span class="n">cache_data</span><span class="p">,</span>
            <span class="n">profiling_verbosity</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">DETAILED</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">verbose_profile</span>
            <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">LAYER_NAMES_ONLY</span><span class="p">,</span>
            <span class="n">tactic_sources</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">tactic_sources</span><span class="p">,</span>
        <span class="p">)</span>

        <span class="c1"># Update timing cache file if needed</span>
        <span class="n">timing_cache</span> <span class="o">=</span> <span class="n">interp_result</span><span class="o">.</span><span class="n">serialized_cache</span>
        <span class="k">if</span> <span class="n">timing_cache</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="o">.</span><span class="n">update_timing_cache</span><span class="p">(</span><span class="n">split_name</span><span class="p">,</span> <span class="n">timing_cache</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">interp_result</span>


<span class="k">def</span> <span class="nf">default_split_function</span><span class="p">(</span>
    <span class="n">model</span><span class="p">:</span> <span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">SplitResult</span><span class="p">:</span>
    <span class="n">splitter_setting</span> <span class="o">=</span> <span class="n">TRTSplitterSetting</span><span class="p">()</span>
    <span class="n">splitter_setting</span><span class="o">.</span><span class="n">use_implicit_batch_dim</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span>
    <span class="n">splitter_setting</span><span class="o">.</span><span class="n">min_acc_module_size</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">min_acc_module_size</span>
    <span class="n">splitter_setting</span><span class="o">.</span><span class="n">use_experimental_rt</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">use_experimental_rt</span>
    <span class="n">splitter</span> <span class="o">=</span> <span class="n">TRTSplitter</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">settings</span><span class="o">=</span><span class="n">splitter_setting</span><span class="p">)</span>
    <span class="n">splitter</span><span class="o">.</span><span class="n">node_support_preview</span><span class="p">()</span>
    <span class="k">return</span> <span class="n">splitter</span><span class="o">.</span><span class="n">generate_split_results</span><span class="p">()</span>


<span class="k">def</span> <span class="nf">create_lower_trt_interpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LowerTrtInterpreter</span><span class="p">:</span>
    <span class="k">return</span> <span class="n">LowerTrtInterpreter</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">default_lower_pass</span><span class="p">(</span>
    <span class="n">create_trt_interpreter</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">LowerSetting</span><span class="p">],</span> <span class="n">LowerTrtInterpreter</span><span class="p">],</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PassFunc</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">lower_pass</span><span class="p">(</span>
        <span class="n">mod</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">,</span> <span class="n">module_name</span><span class="p">:</span> <span class="nb">str</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Create a module transformation pass which lowers an `fx.GraphModule` into a</span>
<span class="sd">        `TRTModule`</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">interpreter</span> <span class="o">=</span> <span class="n">create_trt_interpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">)</span>
        <span class="n">interp_res</span><span class="p">:</span> <span class="n">TRTInterpreterResult</span> <span class="o">=</span> <span class="n">interpreter</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">module_name</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">use_experimental_rt</span><span class="p">:</span>
            <span class="kn">import</span> <span class="nn">io</span>

            <span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>
            <span class="kn">from</span> <span class="nn">torch_tensorrt._TRTModuleNext</span> <span class="kn">import</span> <span class="n">TRTModuleNext</span>

            <span class="k">with</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">()</span> <span class="k">as</span> <span class="n">engine_bytes</span><span class="p">:</span>
                <span class="n">engine_bytes</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">interp_res</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
                <span class="n">engine_str</span> <span class="o">=</span> <span class="n">engine_bytes</span><span class="o">.</span><span class="n">getvalue</span><span class="p">()</span>

            <span class="n">trt_module</span> <span class="o">=</span> <span class="n">TRTModuleNext</span><span class="p">(</span>
                <span class="n">engine_str</span><span class="p">,</span>
                <span class="n">name</span><span class="o">=</span><span class="n">module_name</span><span class="p">,</span>
                <span class="n">input_binding_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">input_names</span><span class="p">,</span>
                <span class="n">output_binding_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">output_names</span><span class="p">,</span>
                <span class="n">target_device</span><span class="o">=</span><span class="n">Device</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cuda:</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">),</span>
                <span class="c1"># cuda_graph_batch_size=lower_setting.cuda_graph_batch_size, # NOTE: Not sure what this is supposed to do</span>
            <span class="p">)</span>
            <span class="k">return</span> <span class="n">trt_module</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="n">trt_module</span> <span class="o">=</span> <span class="n">TRTModule</span><span class="p">(</span>
                <span class="n">engine</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">engine</span><span class="p">,</span>
                <span class="n">input_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">input_names</span><span class="p">,</span>
                <span class="n">output_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">output_names</span><span class="p">,</span>
                <span class="n">cuda_graph_batch_size</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">cuda_graph_batch_size</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="k">return</span> <span class="n">trt_module</span>

    <span class="k">return</span> <span class="n">lower_pass</span>


<span class="nd">@dc</span><span class="o">.</span><span class="n">dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">Lowerer</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Lowers a module using fx2trt.</span>

<span class="sd">    This is a composable class to facilitate fx2trt. A normal fx2trt process</span>
<span class="sd">    composes of the following passes to transform an `fx.GraphModule`:</span>

<span class="sd">        1. trace - use torch.fx to trace the module so we can get the graph</span>
<span class="sd">            representation of the model.</span>
<span class="sd">        2. split - the graph module is split into several submodules,</span>
<span class="sd">            running either via TensorRT, or via regular CUDA.</span>

<span class="sd">    For each split that need to run via TRT, the following passes are</span>
<span class="sd">    invoked:</span>

<span class="sd">        3. `TRTInterpreter` - build the TRT engine for the submodule that</span>
<span class="sd">            can be supported through `TRTInterpreter`.</span>
<span class="sd">        4. Wraps the executable TRT engine into `TRTModule`, which is an `nn.Module`.</span>
<span class="sd">        5. The converted submodule is then set back onto the top-level module</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">lower_pass_manager_builder</span><span class="p">:</span> <span class="n">LowerPassManagerBuilder</span>

    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">create</span><span class="p">(</span>
        <span class="bp">cls</span><span class="p">,</span>
        <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">,</span>
        <span class="n">interpreter_builder</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">create_lower_trt_interpreter</span><span class="p">,</span>
        <span class="n">split_func</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">default_split_function</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Lowerer&quot;</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Instantiate a `Lowerer` instance.&quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">is_aten</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
                <span class="n">lower_pass_manager_builder</span><span class="o">=</span><span class="n">LowerPassManagerBuilder</span><span class="p">(</span>
                    <span class="n">lower_setting</span><span class="o">=</span><span class="n">lower_setting</span><span class="p">,</span>
                    <span class="n">trace_func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">acc_tracer</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span>
                        <span class="n">module</span><span class="p">,</span>
                        <span class="n">inputs</span><span class="p">,</span>  <span class="c1"># type: ignore[arg-type]</span>
                        <span class="n">ast_rewriter_allow_list</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">ast_rewriter_allow_list</span><span class="p">,</span>
                        <span class="n">leaf_module_list</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">leaf_module_list</span><span class="p">,</span>
                    <span class="p">),</span>
                    <span class="n">split_func</span><span class="o">=</span><span class="n">split_func</span><span class="p">,</span>
                    <span class="n">lower_func</span><span class="o">=</span><span class="n">default_lower_pass</span><span class="p">(</span><span class="n">interpreter_builder</span><span class="p">),</span>
                <span class="p">)</span>
            <span class="p">)</span>
        <span class="c1"># proxytensor_trace</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
                <span class="n">lower_pass_manager_builder</span><span class="o">=</span><span class="n">LowerPassManagerBuilder</span><span class="p">(</span>
                    <span class="n">lower_setting</span><span class="o">=</span><span class="n">lower_setting</span><span class="p">,</span>
                    <span class="n">trace_func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">aten_tracer</span><span class="o">.</span><span class="n">opt_trace</span><span class="p">(</span>
                        <span class="n">module</span><span class="p">,</span> <span class="n">inputs</span>
                    <span class="p">),</span>
                    <span class="n">split_func</span><span class="o">=</span><span class="n">split_func</span><span class="p">,</span>
                    <span class="n">lower_func</span><span class="o">=</span><span class="n">default_lower_pass</span><span class="p">(</span><span class="n">interpreter_builder</span><span class="p">),</span>
                <span class="p">)</span>
            <span class="p">)</span>

    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
        <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span>
        <span class="n">additional_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Input</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">fp16_conversion_fn</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">Input</span><span class="p">],</span> <span class="n">Input</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
        <span class="n">lower_setting</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">lower_setting</span>
        <span class="n">atol</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">correctness_atol</span>
        <span class="n">rtol</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">correctness_rtol</span>

        <span class="nd">@validate_inference</span><span class="p">(</span>
            <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">,</span>
            <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">def</span> <span class="nf">do_lower</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
            <span class="n">module</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
            <span class="k">if</span> <span class="p">(</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">lower_precision</span>
                <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
            <span class="p">):</span>
                <span class="n">module</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
                <span class="c1"># A custom conversion function can be passed to the lowerer to</span>
                <span class="c1"># handle inputs with custom types. By default, just handle</span>
                <span class="c1"># tensors and NoneType.</span>
                <span class="k">if</span> <span class="n">fp16_conversion_fn</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                    <span class="n">conversion_fn</span> <span class="o">=</span> <span class="p">(</span>
                        <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
                        <span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
                        <span class="k">else</span> <span class="n">x</span>
                    <span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="n">conversion_fn</span> <span class="o">=</span> <span class="n">fp16_conversion_fn</span>

                <span class="n">inputs</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">conversion_fn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">is_aten</span><span class="p">:</span>
                <span class="n">pm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">build_aten2trt_lower_pipeline</span><span class="p">(</span>
                    <span class="n">inputs</span><span class="p">,</span> <span class="n">additional_inputs</span>
                <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">pm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">build_trt_lower_pipeline</span><span class="p">(</span>
                    <span class="n">inputs</span><span class="p">,</span> <span class="n">additional_inputs</span>
                <span class="p">)</span>
            <span class="n">lower_result</span> <span class="o">=</span> <span class="n">pm</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">lower_result</span>

        <span class="k">return</span> <span class="n">do_lower</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
</pre></div>

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